As computer memory storage and data bandwidth increase, so does the amount and complexity of data that businesses manage. Large-scale distributed storage systems, such as data centers, typically run many business operations. A distributed storage system may be coupled to a number of client computers interconnected by one or more networks. If any portion of the distributed storage system has poor performance or becomes unavailable, company operations may be impaired or stopped completely. Such distributed storage systems seek to maintain high standards for data availability and high-performance functionality.
Within storage systems themselves, file system and storage device-level input/output (I/O) schedulers generally determine an order for read and write operations in addition to providing steps for how the operations are to be executed. For example, non-sequential read and write operations may be more expensive to execute for a storage device (e.g., in terms of time and/or resources) than sequential read and write operations. Therefore, I/O schedulers may attempt to reduce non-sequential operations. In addition, I/O schedulers may provide other functions such as starvation prevention, request merging, and inter-process fairness.
At least the read and write response times may substantially differ between storage devices. Such differences may be characteristic of the technology itself. Consequently, the technology and mechanisms associated with chosen data storage devices may determine the methods used to perform effective I/O scheduling. For example, many current algorithms were developed for systems utilizing hard disk drives (HDDs). HDDs comprise one or more rotating disks, each coated with a magnetic medium. These disks rotate at a rate of several thousand rotations per minute. In addition, an electro-magnetic actuator is responsible for positioning magnetic read/write devices over the rotating disks. The mechanical and electro-mechanical design of the device affects its I/O characteristics. Unfortunately, friction, wear, vibrations and mechanical misalignments may create reliability issues as well as affect the I/O characteristics of the HDD. Many current I/O schedulers are designed to take account for the input/output (I/O) characteristics of HDDs.
One example of another type of storage medium is a Solid-State Drive (SSD). In contrast to HDDs, SSDs utilize solid-state memory to store persistent data rather than magnetic media devices. The solid-state memory may comprise Flash memory cells. Flash memory has a number of features, which differ from that of hard drives. For example, Flash memory cells are generally erased in large blocks before being rewritten or reprogrammed. Flash memory is also generally organized in complex arrangements, such as dies, packages, planes and blocks. The size and parallelism of a chosen arrangement, the wear of the Flash memory over time, and the interconnect and transfer speeds of the device(s) all may vary. Additionally, such devices may also include a flash translation layer (FTL) to manage storage on the device. The algorithms utilized by the FTL can vary and may also contribute to variations in the behavior and/or performance of the device. Consequently, high performance and predictable latencies may not generally be achieved in systems using flash based SSDs for storage while utilizing I/O schedulers designed for systems such as hard drives which have different characteristics.
In view of the above, systems and methods for effectively scheduling read and write operations among a plurality of storage devices are desired.